Antonio Espuña, Moisès Graells and Luis Puigjaner (Editors), Proceedings of the 27th European Symposium on Computer Aided Process Engineering – ESCAPE 27 October 1st - 5th, 2017, Barcelona, Spain © 2017 Elsevier B.V. All rights reserved.

Smart software system solution for model-based hazard identification of complex industrial processes Ján Janošovský, Matej Danko, Juraj Labovský, Ľudovít Jelemenský*

Slovak University of Technology, Institute of Chemical and Environmental Engineering, Radlinského 9, Bratislava 812 37, Slovakia ludovit.jelemensky@stuba.sk

Abstract To satisfy the ever-growing needs of modern civilization, society and industry are experiencing transformation through automation and digitalization. The present work deals with process safety automation issues in chemical industry with a particular focus on computer aided hazard identification based on mathematical modeling and process simulation. In this paper, a smart software system solution combining HAZOP (HAZard and OPerability) study principles and computer simulation of complex industrial processes employing Aspen HYSYS is proposed. An ammonia industrial production unit has been chosen as a case study to demonstrate the applicability and application procedure of the proposed software tool for model-based HAZOP study. The results also indicate that the proposed software tool can supplement process design and intensification studies employing Aspen HYSYS.

Keywords: Computer aided hazard identification, model-based HAZOP study, process simulation, Aspen HYSYS.

1. Introduction Chemical manufacturing as an integral part of industry is significantly influenced by current trends of transformation into Industry 4.0. On the one hand, some spheres of chemical industry have been well prepared for the incoming industrial revolution such as the area of computer aided process design and optimization mainly thanks to the concepts of process systems engineering (PSE) and computer aided process engineering (CAPE) (Gani and Grossmann, 2007). In modern chemical engineering, it is almost unimaginable to design a plant without CAPE/PSE tools. On the other hand, several elements of chemical industry still require further development, e.g. computer aided process safety and risk management techniques. As Kagermann et al. (2013) pointed out in their report, safety issues have been an important consideration in the design of manufacturing facilities for many years; however, modification and automation of safety strategies are still critical factors for the success of Industry 4.0.

Numerous activities have targeted the improvement of conventional process hazard analysis techniques by computer aided approach in the last two decades. Two basic approaches were established: knowledge-based and model-based. The former one facilitates large databases of historically recorded knowledge and experience from past accidents to analyze potentially hazardous events in the examined process (Venkatasubramanian and Vaidhyanathan, 1994; Kidam et al., 2015). The latter one

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utilizes mathematical modeling and simulation of chemical processes as shown for case studies of MTBE plant (Labovský et al., 2007), offshore gas re-injection process (Enemark-Rasmussen et al., 2012) and ammonia synthesis (Labovská et al., 2014). The main advantage of the model-based approach is its capability to identify hazardous events which, if appropriate mathematical model is used, were never observed before but are practically possible.

In the current work, a smart software system solution for model-based hazard identification is proposed. As a simulation platform, commercial process simulator Aspen HYSYS widely used in petrochemical and gas industry has been chosen. The HAZOP (HAZard and OPerability) study principles were adopted to formulate simulation inputs (HAZOP deviations) and to analyze simulation outputs (HAZOP consequences). A set of optimized numerical methods was employed to partially automate the evaluation procedure and to distinguish between hazardous and harmless events. Applicability of the proposed software tool was demonstrated on a case study of an industrial ammonia synthesis unit.

2. Software structure The proposed software tool is constructed from two interconnected components – one for process simulation and one for simulation data analysis. The software structure is schematically shown in Figure 1 and its functionality is further explained on a case study of an ammonia synthesis unit as a part of the ammonia industrial production unit simulated in HYSYS. Mathematical model of the ammonia synthesis unit in HYSYS consisted of an adiabatic fixed-bed catalytic reactor, a feed preheater and a simplified separation unit (Figure 2). Reaction kinetics and its parameters were given by Froment et al. (2010) and registered as HYSYS extension. Design process parameters and setup of the HYSYS model were discussed in our previous work (Janošovský et al., 2017).

Figure 1: Schematic structure of the proposed software tool.

Figure 2: HYSYS model of the ammonia synthesis unit

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2.1. Process simulation module

Mathematical models required for process simulation are currently provided by HYSYS. The process simulation module is connected to a user-selected HYSYS simulation case and extracts data necessary to create HAZOP deviations (Figure 3). These are generated by combining HAZOP guidewords (NONE, MORE, LESS) with process parameters and the value or the value range. Created HAZOP deviations are stored in internal SQLite-based database in the form of quartet <type> <ID> <process parameter> <value>, where <type> determines whether the process parameter belongs to a material or energy stream, or unit operation and <ID> identifies the stream or unit operation within the process simulation environment. Once the final deviation list is completed, the proposed smart HAZOP study proceeds into its simulation phase. In the beginning of the process simulation phase, initial configuration (i.e. design intent) of the HYSYS simulation case is saved. Then, HAZOP deviations are selected one-by-one from the internal database and sent to the HYSYS simulation environment. Due to the sequential character of HYSYS modeling, where each operation unit is represented by a set of mathematical equations and an appropriate numerical algorithm and is simulated individually in an order determined by the HYSYS solver, not every process parameter can be directly changed and information containing the HAZOP deviation details must be therefore properly processed. For this purpose, an apparatus for parameter manipulation has been constructed programmatically inside the HYSYS simulation case. For example, parameter manipulation apparatus for a material stream consists of auxiliary material streams, “Mixer” and “Cooler” unit operations. This configuration allows changing any desired parameter of the material stream selected for HAZOP deviation without violating the HYSYS solver calculation process by simply attaching the target material stream to the manipulation apparatus and mimicking its parameters values except for that selected for the HAZOP deviation. A drawback of this procedure is the ability to investigate HAZOP deviation within the HYSYS simulation environment only in the

Figure 3: HAZOP deviations selection process in the proposed software tool.

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forward direction, i.e. only its consequences and not its causes. When the procedure of parameter manipulation is finished, the HYSYS solver is enabled and new steady state solution is calculated. If the system correctly converged to a new steady state, actual configuration of the HYSYS simulation case (i.e. HAZOP consequence) is assigned to the simulated HAZOP deviation in the internal database for further analysis. The HYSYS simulation case is then restored to its initial configuration and next HAZOP deviation is simulated in the same manner.

For the purpose of data storage from HYSYS simulation case configuration, the class FootPrint was developed. FootPrint stores values of selected relevant process parameters of every material stream, energy stream and unit operation in the actual HYSYS simulation case. When an instance of FootPrint is created and assigned to an HAZOP deviation, its data is accessible without the need of live connection to the HYSYS environment. This feature allows running both software modules simultaneously, i.e. the proposed software tool supports parallel process simulation and simulation data analysis.

2.2. Simulation data analysis module

The simulation data analysis module is a tool providing evaluation of outputs from computer simulations successfully performed in the process simulation module (i.e. HAZOP consequences). It employs several numerical algorithms optimized for the simulated HAZOP consequence investigation in order to identify potential hazards and operability problems caused by the HAZOP deviation. As mentioned above, HAZOP consequence is stored in the internal database in form of a FootPrint instance, which is a HYSYS simulation case configuration “snapshot”, assigned to the HAZOP deviation. In the simulation data analysis module, these FootPrint instances are decomposed and data required for the investigation procedure are extracted. In the next step, two hazard assessment approaches can be applied. The first one utilizes predefined numerical methods such as steady state multiplicity identification, runaway conditions investigation, etc. to partially automate the hazard identification process. The second approach allows the user (e.g. safety engineer, technologist, process control specialist) to adjust the hazard assessment process by defining the process- or equipment-specific threshold values such as the maximum allowed liquid level in a tank, pressure relief valve specifications, etc. When the investigation procedure is finished, hazard assessment results are assigned to a HAZOP consequence and stored in the internal database.

Graphical user interface allows the user to browse through processed HAZOP consequences in the 2D or 3D analysis mode. 2D analysis (Figure 4) provides an overview of process response to HAZOP deviation(s), e.g. for material streams in three main possible forms. The first one monitors one parameter of one material stream as a function of the HAZOP deviation value. The second one is focused on monitoring all parameters of one material stream for one HAZOP deviation value. The third one interprets the behavior of one parameter of all material streams for one HAZOP deviation value. Each of the main forms consists of supplementary methods of detailed analysis, e.g. parametric sensitivity analysis. 3D analysis provides a more complex overview of process response to HAZOP deviation(s). Figure 5 shows one of the possible 3D analyses interpreting the overall process response – complex analysis of the effect of a deviation in the “fresh feed” stream on all material streams in the process. On the x-axis, relative change of the “fresh feed” temperature representing the HAZOP deviation with a value range (from -30 % to +30 %) is depicted. On the y-axis, relative change of temperature of all material streams is depicted. The black color intensity (z-axis) depends on the size of temperature change.

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Figure 4: HAZOP consequences evaluation process in the proposed software tool.

Figure 5: 3D analysis of HAZOP deviation in the proposed software tool.

Together, y- and z-axis represent a set of HAZOP consequences. As shown, various values of the HAZOP deviation caused significantly different HAZOP consequences. For example, when the “fresh feed” temperature was decreased by 20 % (the “fresh feed” temperature deviation of -20 %), the temperature of material streams R101out, R102out and R103out dropped by almost 70 % from design intent. These material streams characterize conditions in the reactor. Therefore, such a dramatic temperature drop indicates termination of the reaction, which was confirmed by a material streams composition analysis. However, when the “fresh feed” temperature was decreased only by 10 %, such dramatic temperature drop in material streams R101out, R102out and R103out and consequent reaction termination were not simulated. This phenomenon was caused by the well documented steady state multiplicity occurring in such synthesis loop configurations (Morud and Skogestad, 1998). The proposed software tool detected this behaviour and provided a multilevel overview of HAZOP deviation propagation paths.

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3. Conclusions The objective of our study is to enable safety engineers to take advantage of process simulators already successfully implemented in modern chemical plants to achieve improved risk assessment and to propose an appropriate methodology for a model-based HAZOP study software tool. Application to a case study of an ammonia synthesis unit modeled in the Aspen HYSYS environment was demonstrated. The steady state multiplicity phenomenon was detected and reported by the presented software tool. The proposed model-based approach allows to identify hazards and operability problems in complex industrial processes considering the HAZOP deviation size and represents an upgrade to the conventional HAZOP study where usually only the existence of deviation is considered and the complicated fault propagation paths are difficult to predict. It was shown that a qualitatively the same but quantitatively different HAZOP deviation can cause qualitatively very different HAZOP consequences, which must be considered in the construction of a suitable software tool for model-based hazard identification. In our future work, extension of the hazard assessment for dynamic simulation outputs and a new simulation engine optimized for safety engineering purposes will be introduced.

Acknowledgements This work was supported by the Slovak Scientific Agency, Grant No. VEGA 1/0749/15 and the Slovak Research and Development Agency APP-14-0317.

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